A Novel Detection of Tibiofemoral Joint Kinematical Space using Graph-based Model of 3D Point Cloud Sequences

Abstract

Abstract The goal of this paper is to tackle the challenge of estimating motion in sequences of 3D point clouds that feature the movement of the knee joint's 3D positions and color attributes. Kinematics and morphology (form) are two important factors in determining the features of flexion and extension. Joints are crucial parts of the linear motion system. Precise estimation of both moments and shape is required to comprehend the functionality of joint surfaces (e.g., the knee). The diagnosis of knee pathologies and treatment of chronic joint diseases such as Osteoarthritis requires an accurate understanding of the in vivo biomechanics of the human knee. However, measuring kinematics in human patients is challenging. The dynamic monitoring of knee motions, whereby generates a realistic bone model that includes and excludes cartilage, can be used to create a novel measurement technique for knee investigations. Such morph kinematic modeling offers the chance to analyze the knee's kinematics and examine interrelations like surfaces in contact or regions. Our purpose is to apply a 3D cloud point database and rigid femur and tibial skeleton to determine motion from the morphology of knee joints. As far as we are aware, this new research paper is the first to utilize both the spatial correlation within each frame (represented by a graph) and the temporal correlation between frames (represented by motion estimation) to enhance the accuracy of joint shape and movement analysis in the context of osteoarthritis. The 3D factor cloud order is in better shape. With the help of a 3D statistical knee database from morphology, our paper proposes a technique for quantifying knee kinematics (motion) (shape).